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Communication Dans Un Congrès Année : 2022

A complete, autonomous and parallel photonic neural network in a semiconductor laser

Résumé

We experimentally demonstrate a fully parallel photonic neural network using spatially distributed modes of a large-area vertical cavity surface-emitting laser (LA-VCSEL). All components of the ANN are fully realized in parallel hardware: 100+ nodes are implemented in a LA-VCSEL, input and output weights are realized via the complex transmission matrix of a multimode fibre and a digital micro-mirror array, respectively. We train the readout weights to perform header recognition (up to 6 bit), XOR classification and digital to analog conversion, and obtain low error rates for all tasks. Our system uses readily available components, is scalable to much larger sizes and to bandwidths in excess of 20 GHz. We also explore more efficient training strategies and different optical weight initializations to increase performance.
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Dates et versions

hal-03549428 , version 1 (31-01-2022)

Identifiants

  • HAL Id : hal-03549428 , version 1

Citer

Anas Skalli, Javier Porte, Nasibeh Haghighi, Stephan Reitzenstein, J Lott, et al.. A complete, autonomous and parallel photonic neural network in a semiconductor laser. Satellite workshop of "International Joint Conference on Neural Networks", Jul 2022, Virtual Event, France. ⟨hal-03549428⟩
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